Counts how many of the indicators in a report measure distress, deficit or disparity and how many measure wellness or strength, and names any group reported on deficits alone.
Arguments
- x
A
welprofresult, whose rows are counted by side.
Value
A list of class rmbl_framing: bygrp (a data frame of
group, wellness, distress, shrdef),
shrdef overall and defonly (the groups with no wellness
indicator).
Details
Walter (2016) shows that statistics about Indigenous peoples cluster
around five Ds: disparity, deprivation, disadvantage, dysfunction and
difference. Results built only of those describe Indigenous peoples as their
problems. This audit does not measure anything about people; it measures the
report: the share of its indicators that are deficit-framed, overall and per
group, so a report can be balanced before it is published. Pair each gap with
a strength-framed level (welprof, dualcont).
References
Walter M (2016). Data politics and Indigenous representation in Australian statistics. In Kukutai T, Taylor J (eds), Indigenous Data Sovereignty: Toward an Agenda, 79-97. ANU Press. doi:10.22459/caepr38.11.2016.05
Walter M, Andersen C (2013). Indigenous Statistics: A Quantitative Research Methodology. Left Coast Press. doi:10.4324/9781315426570
Examples
wb <- indiex("wellbeing")
w <- welprof(
wb,
"group",
"indicator",
"estimate",
"lower",
"upper",
well = "Always or often has a hopeful outlook",
distress = c(
"Rarely or never has a hopeful outlook",
"Life satisfaction rating between 0 and 5",
"Fair or poor perceived health"
)
)
frmaud(w)
#> ╭── Framing audit ────────────────────────────────────────────────────╮
#> │ Groups 3 │
#> │ Wellness indicators 3 │
#> │ Distress indicators 9 │
#> │ Deficit-framed 75% │
#> │ │
#> │ By group (shrdef: the deficit-framed share) │
#> │ group wellness distress shrdef │
#> │ ------------- -------- -------- ------ │
#> │ First Nations 1 3 0.7500 │
#> │ Métis 1 3 0.7500 │
#> │ Inuit 1 3 0.7500 │
#> │ │
#> │ 75% of the indicators reported are deficit-framed. │
#> │ Most of this report measures disparity or deficit; │
#> │ pair each with a strength-framed level. │
#> │ │
#> │ Walter (2016): statistics about Indigenous peoples cluster around │
#> │ disparity, deprivation, disadvantage, dysfunction and difference. │
#> │ This audits the report, not the people it describes. │
#> ╰─────────────────────────────────────────────────────────────────────╯
# your own table: made-up percentages for two communities, with limits
own <- data.frame(
community = rep(c("Lakeside", "Riverbend"), each = 4),
item = rep(c("Hopeful", "Satisfied", "Rarely hopeful", "Low satisfaction"),
2),
pct = c(64, 71, 12, 18, 58, 66, 16, 22),
lo = c(59, 66, 9, 14, 52, 60, 12, 17),
hi = c(69, 76, 15, 22, 64, 72, 20, 27)
)
frmaud(welprof(own, "community", "item", "pct", "lo", "hi",
well = c("Hopeful", "Satisfied"),
distress = c("Rarely hopeful", "Low satisfaction")))
#> ╭── Framing audit ────────────────────────────────────────────────────╮
#> │ Groups 2 │
#> │ Wellness indicators 4 │
#> │ Distress indicators 4 │
#> │ Deficit-framed 50% │
#> │ │
#> │ By group (shrdef: the deficit-framed share) │
#> │ group wellness distress shrdef │
#> │ --------- -------- -------- ------ │
#> │ Lakeside 2 2 0.5000 │
#> │ Riverbend 2 2 0.5000 │
#> │ │
#> │ 50% of the indicators reported are deficit-framed. │
#> │ │
#> │ Walter (2016): statistics about Indigenous peoples cluster around │
#> │ disparity, deprivation, disadvantage, dysfunction and difference. │
#> │ This audits the report, not the people it describes. │
#> ╰─────────────────────────────────────────────────────────────────────╯